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# LangGraph 1.1.0 Release Notes
## Type-Safe Streaming & Invoke
LangGraph 1.1 introduces `version="v2"` — a new opt-in streaming format
that brings full type safety to `stream()`, `astream()`, `invoke()`, and
`ainvoke()`.
### What's changing
**v1 (default, unchanged):** `stream()` yields bare tuples like
`(stream_mode, data)` or just `data`. `invoke()` returns a plain `dict`.
Interrupts are mixed into the output dict under `"__interrupt__"`.
**v2 (opt-in):** `stream()` yields strongly-typed `StreamPart` dicts
with `type`, `ns`, `data`, and (for values) `interrupts` fields.
`invoke()` returns a `GraphOutput` object with `.value` and
`.interrupts` attributes. When your state schema is a Pydantic model or
dataclass, outputs are automatically coerced to the correct type.
### `invoke()` / `ainvoke()` with `version="v2"`
```python
from langgraph.types import GraphOutput
result = graph.invoke({"input": "hello"}, version="v2")
# result is a GraphOutput, not a dict
assert isinstance(result, GraphOutput)
result.value # your output — dict, Pydantic model, or dataclass
result.interrupts # tuple[Interrupt, ...], empty if none occurred
```
With a non-`"values"` stream mode, `invoke(..., stream_mode="updates",
version="v2")` returns `list[StreamPart]` instead of `list[tuple]`.
### `stream()` / `astream()` with `version="v2"`
```python
for part in graph.stream({"input": "hello"}, version="v2"):
if part["type"] == "values":
part["data"] # OutputT — full state
part["interrupts"] # tuple[Interrupt, ...]
elif part["type"] == "updates":
part["data"] # dict[str, Any]
elif part["type"] == "messages":
part["data"] # tuple[BaseMessage, dict]
elif part["type"] == "custom":
part["data"] # Any
elif part["type"] == "tasks":
part["data"] # TaskPayload | TaskResultPayload
elif part["type"] == "debug":
part["data"] # DebugPayload
```
Each stream mode has its own `TypedDict` — `ValuesStreamPart`,
`UpdatesStreamPart`, `MessagesStreamPart`, `CustomStreamPart`,
`CheckpointStreamPart`, `TasksStreamPart`, `DebugStreamPart` — all
importable from `langgraph.types`. The union type `StreamPart` is a
discriminated union on `part["type"]`, enabling full type narrowing in
editors and type checkers.
### Pydantic & dataclass output coercion
When your graph's state schema is a Pydantic model or dataclass,
`version="v2"` automatically coerces outputs to the declared type:
```python
from pydantic import BaseModel
class MyState(BaseModel):
answer: str
count: int
graph = StateGraph(MyState)
# ... build graph ...
compiled = graph.compile()
result = compiled.invoke({"answer": "", "count": 0}, version="v2")
assert isinstance(result.value, MyState) # not a dict!
```
### Backward compatibility
- **Default is still `version="v1"`** — existing code works without
changes.
- To make migration easier, `GraphOutput` supports old-style best-effort
access to graph values and interrupts. Dict-style access
(`result["key"]`, `"key" in result`, `result["__interrupt__"]`) still
works and delegates to `result.value` / `result.interrupts` under the
hood. However, this is **deprecated** and emits a
`LangGraphDeprecatedSinceV11` warning. It will be removed in v3.0 —
migrate to `result.value` and `result.interrupts` at your convenience.
```python
result = graph.invoke({"input": "hello"}, version="v2")
# Old style — still works, but deprecated
result["input"] # delegates to result.value["input"]
result["__interrupt__"] # delegates to result.interrupts
"input" in result # delegates to "input" in result.value
# New style — preferred
result.value["input"]
result.interrupts
```
## Migration Guide
1. **No action required** — `version="v1"` remains the default. All
existing code continues to work.
2. **Adopt v2 incrementally** — Add `version="v2"` to individual
`invoke()`/`stream()` calls to get typed outputs.
3. **Use typed imports** — Import `GraphOutput`, `StreamPart`, and
individual part types from `langgraph.types` for type-safe code.